The Task of Parametric Identification the Interval Models with Nonlinear Parameters

The paper formulates the parametric identification problem of static objects' interval models of as a finding solutions problem of interval nonlinear algebraic equations system in the optimization problem's form with nonlinear and discrete goal function and linear constraints on a continuous set. For the first time the parametric identification method of static objects' interval models based on the interval data analysis is offered and proved. In contrast to the existing ones, the developed method is based on the computational procedures with the self-organization and self-adaptation elements by analogy with the artificial bee colony algorithms. The proposed approach to the models' construction with guaranteed prognostic properties is characterized by lower procedures' time complexity for building such models.